Editing Robotic mapping

{{short description|Discipline related to computer vision and cartography}}
{{more citations needed|date=October 2018}}
'''Robotic mapping''' is a discipline related to [[computer vision]]<ref name="Juan-Antonio2012">{{cite book|author=Fernández-Madrigal, Juan-Antonio|title=Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods: Introduction and Methods|url=https://books.google.com/books?id=JbaeBQAAQBAJ|date=30 September 2012|publisher=IGI Global|isbn=978-1-4666-2105-3}}</ref> and [[cartography]]. The goal for an [[autonomous robot]] is to be able to construct (or use) a map (outdoor use) or [[floor plan]] (indoor use) and to localize itself and its recharging bases or  beacons in it. Robotic mapping is that branch which deals with the study and application of ability to localize itself in a map / plan and sometimes to  
construct the map or floor plan by the autonomous [[robot]].
 
Evolutionarily shaped blind action may suffice to keep some animals alive. For some [[insect]]s for example, the environment is not interpreted as a map, and they survive only with a triggered response. A slightly more elaborated navigation strategy dramatically enhances the capabilities of the robot. [[Cognitive map]]s enable planning capacities and use of current perceptions, memorized events, and expected consequences.

== Operation ==
The robot has two sources of information: the [[idiothetic]] and the [[allothetic]] sources. When in motion, a robot can use [[dead reckoning]] methods such as tracking the number of revolutions of its wheels; this corresponds to the [[idiothetic]] source and can give the absolute position of the robot, but it is subject to cumulative error which can grow quickly.

The [[allothetic]] source corresponds the sensors of the robot, like a camera, a microphone, [[laser]], [[lidar]] or [[sonar]].{{citation needed|date=October 2018}} The problem here is "[[perceptual aliasing]]". This means that two different places can be perceived as the same. For example, in a building, it is nearly impossible to determine a location solely with the visual information, because all the corridors may look the same.<ref>Filliat, David, and Jean-Arcady Meyer. "[http://hal.upmc.fr/docs/00/65/54/73/PDF/Filliat-Meyer_Navigation_Cartes.pdf Map-based navigation in mobile robots:: I. a review of localization strategies]." Cognitive Systems Research 4.4 (2003): 243-282.</ref> 3-dimensional models of a robot's environment can be generated using [[range imaging]] sensors<ref>Jensen, Björn, et al. [https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/82655/1/eth-8118-01.pdf Laser range imaging using mobile robots: From pose estimation to 3D-models]. ETH-Zürich, 2005, 2005.</ref> or [[3D scanner]]s.<ref>Surmann, Hartmut, Andreas Nüchter, and Joachim Hertzberg. "[http://www2.inf.uni-osnabrueck.de/hertzberg/Papers/SurmannEtAlRAAS-2003.pdf An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments]." Robotics and Autonomous Systems 45.3-4 (2003): 181-198.</ref><ref name="Saeed2011">{{cite book|author=Malik, Aamir Saeed|title=Depth Map and 3D Imaging Applications: Algorithms and Technologies: Algorithms and Technologies|url=https://books.google.com/books?id=ouyeBQAAQBAJ|date=30 November 2011|publisher=IGI Global|isbn=978-1-61350-327-0}}</ref>

== Map representation ==
The internal representation of the map can be "metric" or "topological":<ref>[[Sebastian Thrun|Thrun, Sebastian]]. "[https://core.ac.uk/download/pdf/82741752.pdf Learning metric-topological maps for indoor mobile robot navigation]." Artificial Intelligence 99.1 (1998): 21-71.</ref> 
*The metric framework is the most common for humans and considers a two-dimensional space in which it places the objects. The objects are placed with precise coordinates. This representation is very useful, but is sensitive to noise and it is difficult to calculate the distances precisely.
*The topological framework only considers places and relations between them. Often, the distances between places are stored. The map is then a [[Graph (discrete mathematics)|graph]], in which the nodes corresponds to places and arcs correspond to the paths.

Many techniques use probabilistic representations of the map, in order to handle uncertainty.

There are three main methods of map representations, i.e., free space maps, object maps, and composite maps. These employ the notion of a grid, but permit the resolution of the grid to vary so that it can become finer where more accuracy is needed and more coarse where the map is uniform.

== Map learning ==
Map learning cannot be separated from the localization process, and a difficulty arises when errors in localization are incorporated into the map. This problem is commonly referred to as [[Simultaneous localization and mapping]] (SLAM).

An important additional problem is to determine whether the robot is in a part of environment already stored or never visited. One way to solve this problem is by using [[electric beacon]]s, [[Near field communication]] (NFC), [[WiFi]],  [[Visible light communication]] (VLC) and [[Li-Fi]] and [[Bluetooth]].<ref>{{Cite web|url=https://www.indooratlas.com/|title=Your partner in creating smart indoor spaces|website=IndoorAtlas}}</ref>

== Path planning ==
[[motion planning|Path planning]] is an important issue as it allows a robot to get from point A to point B. Path planning algorithms are measured by their computational complexity. The feasibility of real-time motion planning is dependent on the accuracy of the map (or [[floorplan]]), on robot localization and on the number of obstacles. Topologically, the problem of path planning is related to the [[shortest path problem]] of finding a route between two nodes in a [[Graph (discrete mathematics)|graph]].

== Robot navigation {{anchor|Navigation}}==
{{main|Robot navigation}}
Outdoor robots can use GPS in a similar way to [[automotive navigation system]]s.

Alternative systems can be used with [[floor plan]] and beacons instead of [[map]]s for indoor robots, combined with localization wireless hardware.<ref>{{cite web|url = http://ijme.us/issues/spring2014/Z__IJME%20spring%202014%20v14%20n2%20(PDW-3).pdf#page=30|title = An Autonomous Passive RFID-Assisted Mobile Robot System for Indoor Positioning|access-date = 19 October 2015}}</ref> [[Electric beacon]]s can help for cheap robot navigational systems.

== See also ==
* [[Automotive navigation system]]
* [[Domestic robot]]
* AVM Navigator
* [[Dead reckoning]]
* [[Electric beacon]]
* [[GPS]]
* [[Home automation for the elderly and disabled]]
* [[Internet of Things]] (IoT)
* [[Indoor positioning system]]
* [[Map database management]]
* [[Maze Simulator]]
* [[Mobile robot]]
* [[Neato Robotics]]
* [[PatrolBot]]
* [[Real-time locating system]] (RTLS).
* [[Robotics suite]]
* [[Occupancy grid]]
* [[Simultaneous localization and mapping]] (SLAM)
* [[Multi Autonomous Ground-robotic International Challenge]]: A challenge requiring multiple vehicles to collaboratively map a large dynamic urban environment
* [[Wayfinding]]
* [[Wi-Fi positioning system]] (WPS)

== References ==
{{Reflist}}

{{Robotics}}

{{DEFAULTSORT:Robotic Mapping}}
[[Category:Robot navigation| ]]
[[Category:Cartography]]
[[Category:Indoor positioning system]]